Project description
The power of adaptivity in massive networks
Modern networks – social media graphs, biological interaction networks or web graphs – can contain billions of connections, making it impossible to read or process them entirely. Supported by the Marie Skłodowska-Curie Actions programme, the HuNTR project will examine how optimisation problems in huge networks can be solved using limited resources with sublinear-time algorithms. These methods solve problems by looking at a small portion of the network rather than the whole structure. A key focus is understanding the role of adaptivity (whether algorithms that adjust their queries based on previous results are fundamentally more powerful). The project begins with maximum matching and extends to other core problems such as min-cut, max-flow and reachability, ultimately aiming to establish a general theory for monotone graph properties.
Objective
HuNTR investigates how to solve fundamental optimization problems in huge networks using limited resources. In the age of big data, social, web, and biological networks can have billions of edges, making it infeasible to read the entire input. Sublinear-time algorithms address this challenge by examining only a small part of the network while still solving non-trivial optimization problems. Significant progress has recently been made in designing such algorithms. However, with advances in parallel computational models (such as GPU-based, MapReduce, MPC), a natural question arises: How much does adaptivity help in sublinear-time algorithm design?
The primary goal of HuNTR is to resolve this question completely for a large set of optimization problems called monotone graph properties. Several fundamental network problems, such as matching, min-cut, max-flow, and reachability, are in this class and have been a center of attention for the research community for the last 6-7 decades. Toward our goal, (1) I will begin with maximum matching, a cornerstone problem in theoretical computer science with rich mathematical structure, efficient algorithms across various models, and, crucially, a testbed for developing new algorithmic techniques. I will build on my recent breakthrough on non-adaptive matching, which marks the first progress in this direction. (2) These results will then be extended to other important monotone properties and will lead the way for a generalized result for the entire monotone class.
Taken together, these results will establish adaptivity as a central principle in sublinear computation, advance our understanding of key graph problems, and demonstrate its importance in related models such as cut-query and local computation algorithms, where exploration has only recently begun. The techniques developed here will extend beyond this project, advancing sublinear-time algorithms and providing tools applicable to other sublinear models.
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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(opens in new window) HORIZON-MSCA-2025-PF
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B15 2TT Birmingham
United Kingdom
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